5 papers
Barycentric Fused Gromov-Wasserstein Balancing for Causal Inference under Multiple Treatments
Yuki Murakami, Takumi Hattori, Kohsuke Kubota
Estimating heterogeneous single and interaction treatment effects from observational data under multiple simultaneous treatments is crucial for decision-making. To mitigate estimat…
Off-Policy Evaluation and Learning for Survival Outcomes under Censoring
Kohsuke Kubota, Mitsuhiro Takahashi, Yuta Saito
Optimizing survival outcomes, such as patient survival or customer retention, is a critical objective in data-driven decision-making. Off-Policy Evaluation~(OPE) provides a powerfu…
Causal Inference under Threshold Manipulation: Bayesian Mixture Modeling and Heterogeneous Treatment Effects
Kohsuke Kubota, Shonosuke Sugasawa
Many marketing applications, including credit card incentive programs, offer rewards to customers who exceed specific spending thresholds to encourage increased consumption. Quanti…
Bayesian Time-Varying Meta-Analysis via Hierarchical Mean-Variance Random-effects Models
Kohsuke Kubota, Shonosuke Sugasawa, Keiichi Ochiai +1
Meta-analysis is widely used to integrate results from multiple experiments to obtain generalized insights. Since meta-analysis datasets are often heteroscedastic due to varying su…
Multiple Treatments Causal Effects Estimation with Task Embeddings and Balanced Representation Learning
Yuki Murakami, Takumi Hattori, Kohsuke Kubota
The simultaneous application of multiple treatments is increasingly common in many fields, such as healthcare and marketing. In such scenarios, it is important to estimate the sing…